Method and apparatus for monitoring respiratory distress based on autonomic imbalance

ABSTRACT

An example of a system for monitoring and treating respiratory distress in a patient may include signal inputs, a signal processing circuit, and a respiratory distress analyzer. The signal inputs may be configured to receive patient condition signals indicative of autonomic balance of the patient. The signal processing circuit may be configured to process the patient condition signals and to generate patient condition parameters indicative of the autonomic balance using the processed patient condition signals. The respiratory distress analyzer may be configured to determine a state of the respiratory distress using the patient condition parameters, and may include a parameter analysis circuit configured to analyze the autonomic balance of the patient and to determine the state of the respiratory distress using an outcome of the analysis.

CLAIM OF PRIORITY

This application claims the benefit of priority under 35 U.S.C. § 119(e)of U.S. Provisional Patent Application Ser. No. 62/595,174, filed onDec. 6, 2017, which is herein incorporated by reference in its entirety.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to commonly assigned U.S. Provisional PatentApplication Ser. No. 62/595,166, entitled “NON-INVASIVE SYSTEM FORMONITORING AND TREATING RESPIRATORY DISTRESS”, filed on Dec. 6, 2017,which is incorporated by reference in their entirety.

TECHNICAL FIELD

This document relates generally to medical devices and more particularlyto a system that monitors a patient for predicting, detecting, and/ortreating respiratory distress.

BACKGROUND

Obstructive lung diseases, including chronic obstructive pulmonarydisease (COPD) and asthma, are characterized by narrowing airways thatcan make fully expelling air from the lungs difficult. COPD and asthmapatients can experience a significant decline in health (e.g., acuteCOPD exacerbations and asthma attacks), to extents that requirehospitalization. Despite advances in therapeutics, the prevalence ofCOPD and asthma continues to grow.

COPD currently affects nearly 13 million people in the United States andis the third leading cause of death in the country. The overwhelmingprimary cause of COPD is inhalation of cigarette smoke, responsible forover 90% of COPD cases. The economic and social burden of the disease issubstantial and is increasing. The annual economic burden is currentlyestimated to be around $32 billion in the United States alone.Conditions associated with COPD include chronic bronchitis andemphysema. Chronic bronchitis is characterized by chronic cough withsputum production. Airway inflammation, mucus hypersecretion, airwayhyper-responsiveness, and eventual fibrosis of the airway walls resultin significant airflow and gas exchange limitations. Emphysema ischaracterized by destruction of the lung parenchyma, which leads to aloss of elastic recoil and tethering that maintains airway patency.Because bronchioles are not supported by cartilage like the largerairways are, they have little intrinsic support and therefore aresusceptible to collapse when destruction of tethering occurs,particularly during exhalation.

Asthma is similar to chronic bronchitis, though its underlying cause isoften an inherent defect of airway smooth muscle or the inflammatorymilieu, which makes airway smooth muscle hyperreactive. Chronic asthmacan have similar airway wall thickening as in chronic bronchitis,leading to a permanent, irreversible airflow obstruction. Asthma impactsover 18 million adults in the United States. Strikingly, there are 1.6million visits to the emergency rooms resulting from this disease in theUnited States annually. Asthma COPD overlap syndrome (ACOS) is acondition in which a patient has clinical features of both asthma andCOPD. ACOS patients are often among the sickest and most difficult totreat.

The most significant contributor to the economic burden of thesediseases is related to healthcare services for asthma attacks and acuteexacerbations of COPD (AECOPD), mostly emergency care and inpatienthealth care. Despite relatively efficacious drugs that treat COPDsymptoms (e.g., long-acting muscarinic antagonists, long-acting betaagonists, corticosteroids, and antibiotics), a particular segment ofpatients known as “frequent exacerbators” often visit the emergencyrooms and hospitals with exacerbations and also have a more rapiddecline in lung function, poorer quality of life, and greater mortality.Similarly, a group of severe asthmatics are amongst those who visits theemergency rooms most frequently.

Currently, a successful strategy for managing asthma and COPD is theaction plan that follows a “traffic light model” to monitor patientconditions and respond to changes. The traffic light model uses theanalogy of traffic lights to illustrate the seriousness of symptoms(with green, yellow, and red zones) and the action a patient must takein each zone. This technique can be used by patients as well ascaretakers to monitor symptoms. However, this approach has itslimitations. For example, the patient must be compliant and be able torecognize symptoms.

SUMMARY

An example (e.g., “Example 1”) of a system for monitoring and treatingrespiratory distress in a patient may include signal inputs, a signalprocessing circuit, and a respiratory distress analyzer. The signalinputs may be configured to receive patient condition signals indicativeof autonomic balance of the patient. The signal processing circuit maybe configured to process the patient condition signals and to generatepatient condition parameters using the processed patient conditionsignals. The patient condition parameters may be indicative of theautonomic balance of the patient. The respiratory distress analyzer maybe configured to determine a state of the respiratory distress using thepatient condition parameters. The respiratory distress analyzer mayinclude a parameter analysis circuit, which may be configured to analyzethe autonomic balance of the patient and to determine the state of therespiratory distress using an outcome of the analysis.

In Example 2, the subject matter of Example 1 may optionally beconfigured to further include a therapy device configured to deliver oneor more therapies treating the respiratory distress and a controlcircuit configured to control the delivery of the one or more therapiesbased on the state of the respiratory distress.

In Example 3, the subject matter of any one or any combination ofExamples 1 and 2 may optionally be configured to further include astorage device configured to store the state of the respiratory distressdetermined over time, and may optionally be configured such that theparameter analysis circuit is configured to produce and analyze a trendof the state of the respiratory distress.

In Example 4, the subject matter of any one or any combination ofExamples 1 to 3 may optionally be configured such that the parameteranalysis circuit is configured to determine a patient condition metricbeing a linear or nonlinear combination of the patient conditionparameters and to produce one or more respiratory distress indicatorsindicating the state of the respiratory distress based on the patientcondition metric, and the respiratory distress analyzer further includesa notification circuit configured to present the one or more respiratorydistress indicators.

In Example 5, the subject matter of Example 4 may optionally beconfigured such that the parameter analysis circuit is configured toperform at least one of prediction or detection of an exacerbation ofthe respiratory distress based on the patient condition metric, and thenotification circuit is configured to produce an alert notifying aresult of the performance of the at least one of prediction or detectionof the exacerbation.

In Example 6, the subject matter of Example 5 may optionally beconfigured such that the signal processing circuit is configured togenerate patient condition parameters indicative of one or morephysiological markers of asthma, the parameter analysis circuit isconfigured to perform at least one of prediction or detection of anasthma attack, and the notification circuit is configured to produce anasthma alert notifying at least one of the asthma attack being predictedor the asthma attack being detected.

In Example 7, the subject matter of any one or any combination ofExamples 5 and 6 may optionally be configured such that the signalprocessing circuit is configured to generate patient conditionparameters indicative of one or more physiological markers of chronicobstructive pulmonary disease (COPD), the parameter analysis circuit isconfigured to perform at least one of prediction or detection of anexacerbation of COPD, and the notification circuit is configured toproduce a COPD alert notifying at least one of the exacerbation of COPDbeing predicted or the exacerbation of COPD being detected.

In Example 8, the subject matter of any one or any combination ofExamples 1 to 7 may optionally be configured to further include a signalprocessing controller and a signal processing sensor. The signalprocessing controller is configured to receive a processing controlsignal and adjust the processing of the patient condition signals basedon the processing control signal. The signal processing sensor isconfigured to sense a physical state of the patient and to produce theprocessing control signal based on the physical state.

In Example 9, the subject matter of Example 8 may optionally beconfigured such that the signal processing sensor includes one or moreof an activity sensor configured to sense an activity level of thepatient or a sleep sensor configured to sense whether the patient issleeping.

In Example 10, the subject matter of any one or any combination ofExamples 1 to 9 may optionally be configured such that the signal inputsare configured to receive one or more respiratory signals indicative ofrespiratory cycles including inspiratory and expiratory phases and oneor more cardiac signals indicative of cardiac cycles including at leastventricular depolarizations, the signal processing circuit is configuredto process the one or more respiratory signals and the one or morecardiac signals and to generate one or more respiration-mediatedphysiological parameters of the patient condition parameters, and theparameter analysis circuit is configured to determine the state of therespiratory distress based on at least the one or morerespiration-mediated physiological parameters.

In Example 11, the subject matter of Example 10 may optionally beconfigured such that the signal processing circuit is configured togenerate one or more respiration sinus arrhythmia (RSA) parameters ofthe one or more respiration-mediated physiological parameters, the oneor more RSA parameters being one or more measures of the RSA, and theparameter analysis circuit is configured to determine the state of therespiratory distress based on at least the one or more RSA parameters.

In Example 12, the subject matter of any one or any combination ofExamples 1 to 11 may optionally be configured such that the signalinputs are configured to receive one or more blood pressure signalsindicative of blood pressure, one or more cardiac signals indicative ofcardiac cycles including at least ventricular depolarizations and one ormore physical state signals indicative of a physical state of thepatient, the signal processing circuit is configured to process the oneor more blood pressure signals, the one or more cardiac signals, and theone or more physical state signals and to generate one or morebaroreflex sensitivity (BRS) parameters of the patient conditionparameters, the one or more BRS parameters being one or more measures ofthe BRS, and the parameter analysis circuit is configured to determinethe state of the respiratory distress based on at least the one or moreBRS parameters.

In Example 13, the subject matter of Example 12 may optionally beconfigured such that the signal processing circuit is configured togenerate detect levels of physical activity or exertion of the patientfrom the one or more physical state signals and to generate the one ormore BRS parameters each for a plurality of levels of the physicalactivity or exertion.

In Example 14, the subject matter of any one or any combination ofExamples 12 and 13 may optionally be configured such that the signalprocessing circuit is configured to generate detect a type of posturechange of the patient from the one or more physical state signals and tostratify the one or more BRS parameters by the detected type of posturechange.

In Example 15, the subject matter of any one or any combination ofExamples 12 to 14 may optionally be configured such that the signalprocessing circuit is configured to generate detect one or more of amagnitude or a duration of posture change of the patient from the one ormore physical state signals and to stratify the one or more BRSparameters by the detected one or more of the magnitude or the durationof posture change.

An example (e.g., “Example 16”) of a method for monitoring and treatingrespiratory distress in a patient is also provided. The method mayinclude receiving patient condition signals indicative of autonomicbalance of the patient and monitoring the state of the respiratorydistress automatically using a respiratory distress monitoring circuit.The monitoring may include processing the patient condition signals,generating patient condition parameters using the processed patientcondition signals, the patient condition parameters indicative of theautonomic balance of the patient, analyzing the autonomic balance of thepatient using the patient condition parameters, and determining thestate of the respiratory distress using an outcome of the analysis.

In Example 17, the subject matter of Example 16 may optionally furtherinclude delivering one or more therapies treating the respiratorydistress and controlling the delivery of the one or more therapies basedon the state of the respiratory distress.

In Example 18, the subject matter of any one or any combination ofExamples 16 and 17 may optionally further include determining a patientcondition metric being a linear or nonlinear combination of the patientcondition parameters, performing at least one of prediction or detectionof an exacerbation of the respiratory distress based on the patientcondition metric, and producing an alert notifying a result of theperformance of the at least one of prediction and detection.

In Example 19, the subject matter of any one or any combination ofExamples 16 to 18 may optionally further include sensing a physicalstate of the patient and adjusting the processing of the patientcondition signals based on the sensed physical state.

In Example 20, the subject matter of receiving patient condition signalsas found in any one or any combination of Examples 16 to 19 mayoptionally further include receiving one or more respiratory signalsindicative of respiratory cycles including inspiratory and expiratoryphases and one or more cardiac signals indicative of cardiac cyclesincluding at least ventricular depolarizations, and the subject matterof generating the patient condition parameters as found in any one orany combination of Examples 16 to 19 may optionally further includegenerating one or more respiration-mediated physiological parameters ofthe patient condition parameters.

In Example 21, the subject matter of generating the one or morerespiration-mediated physiological parameters as found in claim 20 mayoptionally further include generating one or more respiration sinusarrhythmia (RSA) parameters being one or more measures of the RSA.

In Example 22, the subject matter of receiving patient condition signalsas found in any one or any combination of Examples 16 to 21 mayoptionally further include receiving one or more blood pressure signalsindicative of blood pressure, one or more cardiac signals indicative ofcardiac cycles including at least ventricular depolarizations, and oneor more physical state signals indicative of a physical state of thepatient, and the subject matter of generating the patient conditionparameters as found in any one or any combination of Examples 16 to 21may optionally further include generating one or more baroreflexsensitivity (BRS) parameters being one or more measures of the BRS.

In Example 23, the subject matter of generating the patient conditionparameters as found in claim 22 may optionally further include one ormore of: detecting levels of physical activity or exertion of thepatient from the one or more physical state signals and generating theone or more BRS parameters each for a plurality of levels of thephysical activity or exertion, detecting a type of posture change of thepatient from the one or more physical state signals and stratifying theone or more BRS parameters by the detected type of posture change,detecting a magnitude or a duration of posture change of the patientfrom the one or more physical state signals and stratifying the one ormore BRS parameters by the detected magnitude of posture change, ordetecting a duration of posture change of the patient from the one ormore physical state signals and stratifying the one or more BRSparameters by the detected duration of posture change.

This Summary is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Otheraspects of the disclosure will be apparent to persons skilled in the artupon reading and understanding the following detailed description andviewing the drawings that form a part thereof, each of which are not tobe taken in a limiting sense. The scope of the present disclosure isdefined by the appended claims and their legal equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate generally, by way of example, variousembodiments discussed in the present document. The drawings are forillustrative purposes only and may not be to scale.

FIG. 1 illustrates an embodiment of a circuit for monitoring respiratorydistress of a patient.

FIG. 2 illustrates an embodiment of another circuit for monitoringrespiratory distress of a patient.

FIG. 3 illustrates an embodiment of system for monitoring and treatingrespiratory distress, wherein the circuit of FIG. 1 or FIG. 2 may beused.

FIG. 4 illustrates an embodiment of a method for monitoring and treatingrespiratory distress, such as may be performed by the system of FIG. 3.

FIG. 5 illustrates an embodiment of a system for monitoring respiratorydistress.

FIG. 6 illustrates an embodiment of a system for closed-loop therapydelivery for treating respiratory distress.

FIG. 7 illustrates an embodiment of a system of non-invasive monitoringdevices for monitoring respiratory distress.

FIG. 8 illustrates an example of short-term heart rate variability (HRV)throughout a respiration cycle under healthy and diseased conditions.

FIG. 9 illustrates another example of short-term HRV throughout arespiration cycle under healthy and diseased conditions.

FIG. 10 illustrates an embodiment of a method for monitoring respiratorydistress based on an RSA metric.

FIG. 11 illustrates an example of a method for monitoring RSA usingelectrocardiographic (ECG) and accelerometer signals.

FIG. 12 illustrates an example of RSA information acquired using themethod of FIG. 11 and allowing for predicting or detecting exacerbationof respiratory distress.

FIG. 13 illustrates an example of BRS under healthy and diseasedconditions.

FIG. 14 illustrates an embodiment of a method for monitoring respiratorydistress based on a BRS metric.

FIG. 15 illustrates an example of a method for monitoring BRS using ECG,blood pressure, and accelerometer signals.

FIG. 16 illustrates an example of a method for monitoring BRS using ECG,heart sound, and accelerometer signals.

FIG. 17 illustrates an example of BRS information acquired using themethod of FIG. 15 of FIG. 16 and allowing for predicting or detectingexacerbation of respiratory distress.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which is shown byway of illustration specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that the embodiments may be combined, or that otherembodiments may be utilized and that structural, logical and electricalchanges may be made without departing from the spirit and scope of thepresent invention. References to “an”, “one”, or “various” embodimentsin this disclosure are not necessarily to the same embodiment, and suchreferences contemplate more than one embodiment. The following detaileddescription provides examples, and the scope of the present invention isdefined by the appended claims and their legal equivalents.

This document discusses, among other things, systems and methods formonitoring respiratory distress, including detection and/or predictionof exacerbations of pulmonary diseases affecting airways, such as asthmaand chronic obstructive pulmonary disease (COPD). Exacerbations of suchdiseases create significant economic burden on the healthcare system.Patients experiencing these episodes tend to deteriorate over time.Thus, there is a need for consistent and accurate means to monitorpatient status and detect worsening systems prior to an episoderequiring hospitalization.

Studies have shown variable durations of increased signs and/or symptomsleading up to exacerbations in asthma patients. However, there arenoticeable trends in signs and/or symptoms on average approximately oneweek prior to the episode. One study found that patients (at least 16years old, from 11 countries, structured interviews of 3415 adults)reported a mean time from the first appearance to peak of signs andsymptoms of 5.1 days (range: <30 minutes to >2 weeks) and a meaninterval from the peak of symptoms to recovery of 6.2 days. Anotherstudy found that the mean maximal decrease in the morning PEF was 16% to20%. This decrease was gradual, from day-10 to day-3, followed by a morerapid decrease. It is believed that fast onset episodes are due totriggers such as allergens and irritants, while slow onset episodes aredue to faults in management. Analysis of fatal asthma attacks showedthat 80% of them had slow onset.

Similar to asthma patients, COPD patients generally have a slow onset ofsigns and/or symptoms which has been reported to be approximately 1-2weeks in duration, with symptoms steadily increased from 2 weeks priorto exacerbation, with a sharp rise during the last week. Respiratorytract infection is one of the most common triggers for COPDexacerbation, accounting for a majority of exacerbations.

There is a need for effective monitoring of patients to provide areliable indication of their conditions and monitor respiratory distressto (1) warn the patients and provide a window to administer therapy toprevent hospitalizations, and/or (2) to alert patients and appropriatemedical personnel of the onset of an exacerbation to get the patient theappropriate medical care. There is a need for reducing occurrence ofevents such as asthma attacks and acute exacerbations of COPD (AECOPD)for these patients, as such events are the primary contributor to theeconomic and social burden of respiratory diseases.

Failure to provide the correct type and/or duration of therapy for anasthma attack or acute exacerbation of COPD can prevent recovery, delayrecovery, or result in an additional asthma attack or acute exacerbationof COPD soon after the initial disease event. Hence there is a need tomonitor treatment of patients during recovery from an asthma attack andacute exacerbation of COPD. In one example, there is a need to monitorpatients during hospitalization and emergency room visits for asthmaattack and acute exacerbation of COPD to ensure the recovery therapy iseffective and to prevent premature discharge. In another example, thereis a need to monitor patients during recovery away from a hospitalsetting (e.g., at-home, or nursing home) to ensure compliance andeffectiveness of the recovery therapy. Monitoring during recovery may bedifferent from or the same as monitoring apart from recovery. Monitoringduring recovery may include more frequent data gathering, gathering ofadditional or different parameters, and/or more frequent reporting to acaregiver (e.g., medical professional, or at-home caregiver).

As a patient's signs and/or symptoms worsen preceding an exacerbation,non-invasive devices can be used to capture symptomatic andphysiological changes to warn the patient of declining health and/oralert appropriate personnel in the event of an exacerbation episode.Signs associated with airway obstruction such as coughing, wheezing(lung sounds), increased respiration rate, and lung hyperinflation canbe captured by monitoring the patient using a system of non-invasivesensors. In addition, measures of autonomic activity can be monitoredover time to assess patient condition and send a warning for a likelyexacerbation based on signals acquired and/or alert appropriatepersonnel in the event of an exacerbation so that the patient canpromptly receive medical attention.

In one example, the present subject matter provides a system thatincludes a noninvasive or minimally invasive system for monitoring COPDand asthma patients to provide a reliable indication of their conditionand detect (1) worsening signs and/or symptoms, to warn patients andprovide a window to administer therapy to prevent hospitalizations,and/or (2) exacerbations, in the event of rapid onset of signs and/orsymptoms, to alert appropriate medical personnel of the onset of anexacerbation to get the patient the appropriate medical care. Thissystem can also identify changes in the patient's condition subsequentto therapy to indicate a need for adjustment or termination of thetherapy. In various embodiments, the system can include one or moresensors that can indirectly or directly sense symptoms and/orphysiological signals indicative of worsening condition and/or onset ofan exacerbation. The system can also contain a processing unit toprocess incoming signals and extract appropriate signal information. Theprocessing unit can execute an algorithm to process the incoming signalsalong with stored data (e.g., trend data) to assess the patient'scondition. In the event of worsening signs and/or symptoms preceding orat the onset of an exacerbation, the system can notify the patient,caregiver, and/or appropriate medical personnel.

A biomarker of respiratory distress, such as respiration sinusarrhythmia (RSA), can be captured invasively or noninvasively throughdirect or indirect means to provide an indication of the patient'scondition and provide a warning when the condition becomes worse. RSA isa short term measure of heart rate variability (HRV), and is aphysiological indicator that may have implications for monitoringpulmonary diseases such as asthma and COPD. RSA can be used to assesscardiac autonomic function, and can represent the transfer function fromrespiration rate to cardiac cycle length (e.g., time intervals betweensuccessive R-waves, the R-R intervals). During inspiration, inhibitorysignals decrease vagal nerve activity, resulting in increased heart rateand decreased RSA. Conversely, during expiration, increasing vagus nerveactivity results in decreased HR and increased RSA.

Asthma and COPD are associated with impairment in the autonomic balance(coordination between the sympathetic and parasympathetic nervoussystems) which can be reflected by monitoring HRV and/or RSA. Thisimbalance, demonstrated in COPD patients, manifests as an elevation insympathetic activity and a withdrawal of parasympathetic activity. Instudies with asthma patients, the imbalance in the autonomic nervoussystem results from the hyperactivity of the parasympathetic branchcausing bronchial constriction. In addition, the dysfunction orhypoactivity of the sympathetic branch has been tied to the severity ofasthma. These alterations in autonomic balance can be monitored forindividual patients to see when RSA deviates from a baseline value,either increasing or decreasing. Since this measure is based on therespiratory signal, RSA can continuously be evaluated to providefeedback on the patient's condition that does not require the patient tobe performing a specific task or at an in-office assessment. Additionalrespiration mediated signals could be captured as a surrogate to heartrate including blood pressure, blood flow/perfusion, heart sounds,direct neural recordings, and blood gas (02 and CO2) concentrations.

In one example, the present subject matter provides a system that canmonitor respiration-mediated signals in patients with pulmonary diseasesthat restrict airflow, such as asthma, COPD, chronic bronchitis, andemphysema. Heart rate responses to respiration can be captured throughinvasive or non-invasive means to monitor the patient's condition. Thissystem can alert the patient or caretaker of worsening conditions and/orthe need for intervention. This system can also identify changes in thepatient's condition subsequent to therapy to indicate a need foradjustment or termination of the therapy. In various embodiments, thesystem can include one or more sensors that can directly or indirectlysense a respiratory signal and another physiological signal modulated byrespiration. These signals can be processed to extract period of therespiration cycle including inspiration and expiration phases. Thecorresponding respiratory periods of the cardiac signal can be processedto extract heart rate and inter-beat intervals (the R-R intervals). Analgorithm can be executed to calculate respiration-mediated signalindices and provide a measure of the patient's condition.

Arterial baroreflex (also referred to as baroreceptor reflex) isimportant for hemodynamic stability and cardioprotection, and is astrong prognostic indicator. The carotid and aortic baroreceptors detectchanges in pressure, providing negative feedback to the closed-loopsystem for regulating blood pressure. In a healthy person, whenbaroreceptor activation increases due to a blood pressure increase,efferent parasympathetic activity increases to lower blood pressurethrough slowing the heart rate and causing peripheral vasodilation.Baroreflex sensitivity (BRS), defined as the change in inter-beatinterval (IBI) in ms/mmHg, provides an indication of the function ofthis closed-loop system and can be measured from standard heart rate andblood pressure monitoring techniques.

Asthma and COPD are associated with impairment in the autonomic balancewhich can be reflected by monitoring BRS, either through spontaneousmeasures or clinical evaluations. This imbalance, as demonstrated inCOPD patients, manifests as an elevation in sympathetic activity and awithdrawal of parasympathetic activity resulting in decreased BRS. Instudies with asthma patients, the imbalance in the autonomic nervoussystem results from the hyperactivity of the parasympathetic branch.Treatment has been shown to decrease BRS as the cardiovagalresponsiveness decrease and sympathetic activity increases. Thesealterations in autonomic balance can be monitored for individualpatients to see when BRS deviates from a baseline value, eitherincreasing or decreasing. Since this measure is based on the respiratorysignal, BRS can continuously be evaluated to provide feedback on thepatient's condition that does not require the patient to be performing aspecific task or at an in-office assessment.

In the event of an AECOPD or asthma attack, the patient has heightenedsympathetic nervous system activity, which causes in increase in bloodpressure and heart rate. The increased blood pressure in turn activatesbaroreceptors which down-regulate sympathetic outflow, restoringhomeostasis. This baroreflex can increase or decrease blood pressure. Ina healthy person who transitions abruptly from a supine to standingposition, pooling of blood in the lower extremities causes an immediatearterial blood pressure reduction, which in turn activates baroreceptorsto increase sympathetic outflow causing a blood pressure and heart rateincrease, again restoring homeostasis. These are healthy compensatoryresponses. An attenuated baroreceptor response causes a reduced anddelayed heart rate and blood pressure response to a posture change (orany physical activity that typically activate the baroreceptors).

Natural BRS response has variability due to respiration, physical andmental stressors, which is evident in everyday activities. Thesedynamics can be used as an indicator of baroreceptor function, and canbe used to monitor patients with airflow limitations. The dynamic BRSresponse can be captured using beat-to-beat sensitivity to investigatechanges in heart rate and blood pressure for each cardiac contraction.One method for measuring BRS is measuring spontaneous BRS. SpontaneousBRS can be measured through consecutive beats that are characterized bysimultaneous increases or decreased in blood pressure and R-R interval.BRS is then calculated as the average of the linear regression slopesdetected for each sequence over a given time interval. Examples formeasuring this dynamic response include measuring through monitoringrespiration and/or physical activity.

Similar to respiratory sinus arrhythmia and diminished HRV, diminishedBRS is evident in COPD and asthma patients. The dynamic spontaneous BRScan be captured through analysis of blood pressure and heart rate duringa respiratory cycle. This is because there is always spontaneous bloodpressure variability (BPV) due to respiration. Respiration induces HRVby mediation of the arterial baroreflex and by direct mechanicalmodulation of the SA node pacemaker properties. Using inspiration andexpiration, consecutive increases or decreases can be captured tocalculate BRS for monitoring the patient.

Moment-to-moment regulation of blood pressure through the baroreflex isreduced during exercise in comparison to rest. BRS decreases duringexercise because the body's operating point on the curve of heart rateagainst blood pressure has shifted away from the maximal sensitivitypoint at the center of the curve (at rest condition). The shift movesthe “set point” of blood pressure to a higher level with lesssensitivity to changes in blood pressure. This change in baroreflexdepends on exercise intensity. As the exercise intensity increases (asthe heart rate increases), the response curve changes with the lowestsensitivity at the highest exercise intensity where the subjectsmaintained a heart rate of 150 beat per minute (bpm). As the exerciseintensity increases, the operating point progressively moves away fromthe center point towards the upper threshold of the curve. Pulmonarydisease affecting airways such as asthma and COPD are associated withalterations in autonomic function. This dysfunction can be investigatedthrough physical activity by monitoring the baroreflex. Exercise alonecauses a decrease in BRS, and exercise compounded with airway limitationmay lead to a more significant reduction in BRS. By coupling activityand BRS monitoring, the baroreflex can be evaluated at a higheroperating point (due to exercise) for monitoring the patient's conditionand evaluating the need for therapeutic intervention.

In one example, the present subject matter provides a system forambulatory assessment of baroreceptor response. The patient'sbaroreceptor response to events such as respiration or activity can beused for monitoring the patient's condition related to pulmonarydisease, such as asthma and COPD. Heart rate, blood pressure,respiration, and activity signals can be sensed through invasive ornon-invasive means, through direct or indirect measures. This system canalert the patient or caretaker of worsening conditions or the need forintervention. This system can also identify changes in the patient'scondition subsequent to therapy to indicate a need for adjustment ortermination of the therapy. In various embodiments, this system caninclude an activity sensor, a respiration sensor, and an additionalsensor for measuring baroreceptor response. The system can include aprocessor to process the signals produced by these sensors to analyzethe spontaneous baroreceptor response during respiration as detected bythe respiration sensor and/or during physical activity as detected bythe activity sensor. The processor can execute an algorithm to calculatebaroreceptor response indices to provide a measure of the patient'scondition.

FIG. 1 illustrates an embodiment of a respiratory distress monitoringcircuit 100. Respiratory distress monitoring circuit 100 can includesignal inputs 101, a signal processing circuit 102, and a medicalcondition analyzer 103. In various embodiments, respiratory distressmonitoring circuit 100 can be implemented as part of a system formonitoring and/or treating a patient suffering from one or more medicalconditions including respiratory distress. Examples of the respiratorydistress include COPD and asthma.

Signal inputs 101 can receive patient condition signals indicative of astate of the respiratory distress of the patient. Signal processingcircuit 102 can process the patient condition signals and generatepatient condition parameters using the processed patient conditionsignals. The patient condition parameters are indicative of the state ofthe respiratory distress of the patient. Respiratory distress analyzer103 can determine the state of the respiratory distress using thepatient condition parameters. Respiratory distress analyzer 103 caninclude parameter inputs 104 and a parameter analysis circuit 105.Parameter inputs 104 can include a physiological marker input 106 toreceive one or more physiological marker parameters of the patientcondition parameters and an other parameter input 107 to receive one ormore other parameters of the patient condition parameters that can beused in the determination of the state of the respiratory distress. Theone or more physiological marker parameters represent one of morephysiological markers for the respiratory distress and can be one ormore quantitative measures of the respiratory distress. Parameteranalysis circuit 105 can analyze the patient condition parametersreceived from signal processing circuit 102 and determine the state ofthe respiratory distress using an outcome of the analysis.

In one embodiment, the patient condition signals include signalsacquired by non-invasive sensors such that respiratory distressmonitoring circuit 100 can be used in a non-invasive patient monitoringand/or treatment system. In one embodiment, the patient conditionsignals include signals indicative of autonomic balance of the patient,and parameter analysis circuit 105 can to analyze the autonomic balanceof the patient and determine the state of the respiratory distress basedon a state of the autonomic balance. Examples of measures of autonomicbalance include RSA and BRS. In one embodiment, parameter analysiscircuit 105 can produce a patient condition metric being a linear ornonlinear function of the patient condition parameters and predict anexacerbation of the respiratory distress based on the patient conditionmetric. Respiratory distress analyzer 103 can produce an alert notifyingthe prediction of the exacerbation.

FIG. 2 illustrates an embodiment of an embodiment of a respiratorydistress monitoring circuit 200, which can represent an example ofrespiratory distress monitoring circuit 100. Respiratory distressmonitoring circuit 200 can include signal inputs 201, a signalprocessing circuit 202, a signal processing controller 213, and arespiratory distress analyzer 203.

Signal input 201 can represent an example of signal input 101 and canreceive the patient condition signals indicative of the state of therespiratory distress. The patient condition signals can include one ormore signals sensed by one or more sensors and indicative physiologicalmarkers of the respiratory distress and one or more other signals thatcan otherwise be used by respiratory distress analyzer 203 indetermining the state of the respiratory distress. Signal processingcircuit 202 can represent an example of signal processing circuit 102and can process the patient condition signals received by signal inputs201 and can generate patient condition parameters indicative of thestate of the respiratory distress. The patient condition parameters caninclude one or more physiological marker parameters that are indicativeof the physiological markers of the respiratory distress and can allowfor detection and/or prediction of exacerbation. In various embodiments,a sensor or a combination of sensors can be employed to monitor symptomsand physiological markers indicative of the state of the respiratorydistress. Examples of physiological markers of respiratory distress thatcan be signs for exacerbation include:

-   -   (i) Respiration rate;    -   (ii) Lung sounds, including chest sounds that can be examined by        tapping chest and using a microphone to capture the response        tone;    -   (iii) Cough;    -   (iv) Wheezing;    -   (v) Respiration flow characteristics, such as FEV1, FEV3, FEV6,        TC, FVC, MV, TLC, flow rate, volume measures, and any        combination of these parameters;    -   (vi) Oxygen Saturation;    -   (vii) Central cyanosis;    -   (viii) Activity levels;    -   (ix) Sleep quality;    -   (x) Body temperature;    -   (xi) Heart rate;    -   (xii) Heart rate variability (HRV), including heart rate        acceleration and deceleration capacity;    -   (xiii) Respiration sinus arrhythmia (RSA);    -   (xiv) Blood pressure;    -   (xv) Blood pressure variability;    -   (xvi) Baroreceptor reflex sensitivity (BRS);    -   (xvii) Galvanic skin response;    -   (xviii) Direct neural measures including neural respiratory        drive index (NRDI), parasternal EMG, diaphragm EMG; and    -   (xix) Chemical indicators of stress and inflammation.

In various embodiments, the one or more physiological marker parameterscan each indicate and/or be a measure of one or more of thesephysiological markers. Table 1 includes a more complete list of suchphysiological markers with rationale for each marker.

Respiratory distress analyzer 203 can represent an example ofrespiratory distress analyzer 103 and can analyze the patient conditionparameters generated by signal processing circuit 202 and determine thestate of the respiratory distress based on an outcome of the analysis.Respiratory distress analyzer 203 can include parameter inputs 204, aparameter analysis circuit 205, a storage device 214, and a notificationcircuit 215. Parameter inputs 204 can include a physiological markerinput 206 and an other parameter input 207. Physiological marker input206 can receive one or more physiological marker parameters generated bysignal processing circuit 202, such as one or more parameters eachindicative or being a measure of one or more physiological markerslisted above (i-xix) or in Table 1. Other parameter input 107 canreceive one or more other parameters that can be used in thedetermination of the state of the respiratory distress, includinginformation entered by the patient and/or the user. In this document, a“user” can include a physician, other medical professional, or caregiverwho attends the patient including monitoring and/or treating the patientusing the present system. In some example, the “user” can also includethe patient, such as when the patient is allowed to adjust certainoperations of the system.

Parameter analysis circuit 205 can represent an example of parameteranalysis circuit 105 and can determine the state of the respiratorydistress based on the patient condition parameters received by parameterinputs 204. In one embodiment, parameter analysis circuit 205 determinesa patient condition metric being a linear or nonlinear combination ofthe patient condition parameters, and produces one or more respiratorydistress indicators indicating the state of the respiratory distressbased on the patient condition metric. The patient condition parametersincludes at least the one or more physiological marker parameters.Storage device 214 can store the state of the respiratory distressdetermined by parameter analysis circuit 205 over time. In variousembodiments, parameter analysis circuit 205 can produce and analyze atrend of the state of the respiratory distress using the stored states.The trend allows respiratory distress analyzer 203 to identify changesin the patient's condition including changes in the state of therespiratory distress.

Notification circuit 215 can present the one or more respiratorydistress indicators produced by parameter analysis circuit 205 to thepatient and/or the user (e.g., through a user interface of a systemillustrated in one of FIGS. 5-7 and discussed below). Notificationcircuit 215 can include a classification circuit 216 and an alertcircuit 217. Classification circuit 216 can stratify a risk forexacerbation of the respiratory distress for the patient. The risk canbe categorized based on individual characteristics of the patient,including, for example, diet, pollen levels, allergies, activity levels,disease history, and/or sleep quality. The risk stratification can allowrespiratory distress analyzer 203 to respond to worsening signsdifferently for a patient currently in a low risk category versus apatient currently in a high risk category for the exacerbation, forexample, in determine whether and how to notify the patient and/or theuser. Alert circuit 217 can produce an alert notifying a need formedical intervention based on the one or more respiratory distressindicators. In one embodiment, alert circuit 217 produces the alertbased on the one or more respiratory distress indicators and thepatient's risk category stratified by classification circuit 216.Depending on the stratified risk category, alert circuit 217 can producean alert notifying a detection of the respiratory distress and adistinct alert notifying a prediction of the respiratory distress,and/or distinct alerts notifying different risk categories.

Signal processing controller 213 can receive a processing control signaland adjust the processing of the patient condition signals based on theprocessing control signal. The processing control signal can include asignal indicative of a physical state of the patient, such as a signalindicating an activity level of the patient (e.g., sensed from thepatient using an activity sensor) or a signal indicating whether thepatient is sleeping (e.g., sensed from the patient using a sleepsensor). For example, the activity or sleep sensor may trigger samplingfor heart rate, respiration rate, and lung sounds and processing ofthese signals only when the patient is sleeping or at rest. In variousembodiments, signal processing controller 213 can adjust a sampling rateof signal processing circuit 202 based on the processing control signaland/or activate or deactivate signal processing circuit 202 based on theprocessing control signal. In various embodiments, signal processingcontroller 213 can also activate or deactivate other portions ofrespiratory distress monitoring circuit 200 and/or other portions of thepresent system (e.g., monitoring devices acquiring the patient conditionsignals) based on the processing control signal.

FIG. 3 illustrates an embodiment of system 320 for monitoring andtreating the respiratory distress. Respiratory distress monitoringcircuit 100 or 200 can be implemented in system 320. For monitoringpurposes, system 320 includes at least one or more monitoring devices321 and a respiratory distress monitoring circuit 300. For monitoringand therapeutic purposes, system 320 can include monitoring device(s)321, respiratory distress monitoring circuit 300, a control circuit 322,and a therapy device 323. Monitoring device(s) 321 acquire the patientcondition signals. For example, monitoring device(s) 321 can include oneor more sensors to sense one or more signals related to the patient'smedical condition including the state of respiratory distress andproduce the one or more sensor signals of the patient condition signals.Therapy device 323 can deliver one or more therapies treating therespiratory distress, including prevention of a predicted exacerbation.Control circuit 322 can control the delivery of the one or moretherapies based on the state of the respiratory distress as determinedby respiratory distress monitoring circuit 300. Examples of respiratorydistress monitoring circuit 300 include medical condition monitoringcircuits 100 and 200. In addition to, or in place of, delivering the oneor more therapies, system 320 can also recommend to the patient or theuser actions to take based on the patient's conditions including thestate of the respiratory distress. In various embodiments, system 320 isa closed-loop therapy system, with monitoring device(s) 321 sensingeffects of delivery of the one or more therapies for adjusting thedelivery based on the effects.

In one embodiment, monitoring device(s) 321, respiratory distressmonitoring circuit 300, control circuit 322, and therapy device 323 areintegrated into a single medical device. In other embodiments,monitoring device (s) 321, respiratory distress monitoring circuit 300,control circuit 322, and therapy device 323 can be implemented as two ormore medical devices communicatively coupled to each other to formsystem 320. These two or more devices can be any combination ofimplantable, wearable, handheld, and/or remote devices.

In various embodiments, system 320 can include an implantable medicaldevice that includes an implantable drug pump and/or a neuromodulationdevice (e.g., for delivering vagus nerve stimulation, pulmonary vagalfiber block therapy, and/or superior laryngeal nerve block therapy) tobe used as therapy device 323. In various embodiments, such as when thepatient is not connected to a therapy device, system 320 can send alertsor notifications to the patient and/or the user when the conditionincluding the state of the respiratory distress is worsening and/or whenmedical intervention becomes necessary or recommendable. System 320 candetect and/or predict an exacerbation of the respiratory distress basedon early or late stage of worsening symptoms and slow or rapid onset.For example, system 320 can send early stage warnings to the patientonly and late stage warnings to the user in addition to the patient. Inanother example, system 320 can notify the patient in a slow onset forthe patient to take action but notify the user in addition to thepatient in a rapid onset. In various embodiments, system 320 can be usedin combination with a medical condition management plan for the patientto follow, for example, by notifying the patient of the state and/orrisk category of the respiratory distress such that the patient canadjust medication and/or daily activities accordingly.

In various embodiments, signal inputs 101 or 201 can receiveenvironmental information related to the state of the respiratorydistress, and parameter analysis circuit 105 or 205 can determine thestate of the respiratory distress based on one or more physiologicalmarker parameters and the received environmental information. Examplesof the environmental information include time of day, time of year, GPSlocation, pollen levels, pollution levels, humidity levels, webinformation on local news, hospital admissions, and/or information ondisease epidemic (e.g., flu or cold). The environmental information canbe sensed using one or more sensors of monitoring device(s) 321 and/orprovided by external sources.

In various embodiments, signal inputs 101 or 201 can receive user-inputdata related to the state of the respiratory distress. The user-inputdata can be entered by the patient and/or the user. Parameter analysiscircuit 105 or 205 can determine the state of the respiratory distressbased on the one or more physiological marker parameters and one or moreof the received environmental information and the user-input data.Examples of the user data include a log of the patient's actual asthmaattacks and/or COPD exacerbations, pharmaceutical use information,and/or allergies. In various embodiments, notification circuit 215 canprovide the patient with custom recommendations based upon theuser-input data.

In various embodiments, circuits of system 320, including its variousembodiments discussed in this document, may be implemented using acombination of hardware and software. For example, the circuits may beimplemented using an application-specific circuit constructed to performone or more particular functions or a general-purpose circuit programmedto perform such function(s). Such a general-purpose circuit includes,but is not limited to, a microprocessor or a portion thereof, amicrocontroller or portions thereof, and a programmable logic circuit ora portion thereof.

FIG. 4 illustrates an embodiment of a method 430 for monitoring andtreating respiratory distress. In one embodiment, method 430 can beperformed using system 320.

At 431, patient condition parameters are received and analyzed. Thepatient condition parameters can include one or more physiologicalmarker parameters each indicative or being a measure of one or morephysiological markers of the respiratory distress, such as those listedabove (i-xix) or in Table 1. In some embodiments, the patient conditionparameters can also include other parameters useable in determining thestate of the respiratory distress, such as inputs from the patientand/or the user.

At 432, the state of the respiratory distress is determined based on anoutcome of the analysis of the patient condition parameters. In oneembodiment, a patient condition matrix is produced as a liner ornonlinear function of the patient condition parameters, and one or moreindicators of the state of the respiratory distress are produced basedon the patient condition metric.

If the state of the respiratory distress (e.g., a quantitative measureof the state) does not exceed a threshold at 433, method 430 continuesfrom 431 again. If the state of the respiratory distress exceeds thethreshold at 433, an alert is produced to notify the patient and/or theuser, and/or one or more therapies treating the respiratory distress aredelivered, at 434. Method 430 can continue from 431 again to monitor thestate of the respiratory distress including the effect of the deliveryof the one or more therapies and/or other medical intervention resultingfrom the alert.

FIG. 5 illustrates an embodiment of a system 520 for monitoringrespiratory distress. System 520 can represent an example of system 320(with monitoring functions only). As illustrated in FIG. 5, system 520can include monitoring devices 538, a portable device 542, a network 545communicatively coupled to portable device 542 via a wired or wirelesscommunication link 543, and a medical facility 547 communicativelycoupled to network 545. Respiratory distress monitoring circuit 300 canbe distributed in portable device 542 and/or network 545. In variousembodiments, portable device 542 can be implemented as a dedicateddevice or in a generic device such as a smartphone, a laptop computer,or a tablet computer. Monitoring devices 538 can include monitoringdevices 321 each being an implantable or non-implantable sensorcommunicatively coupled to portable device 542 via a wired or wirelesslink. Information such as the patient condition signals, the patientcondition parameters, and/or the one or more respiratory distressindicators can be received and/or produced by portable device 542 andtransmitted to network 545 via communication link 543 to be stored,further analyzed, and/or inform the user. When the patient's medicalcondition including the state of the respiratory distress (e.g., asdetermined in portable device 542 or network 545) indicates that thepatient needs medical attention, a notification will be transmitted tomedical facility 547 from network 545.

FIG. 6 illustrates an embodiment of a system 620 for closed-loop therapydelivery for treating respiratory distress. System 620 can representanother example of system 320. As illustrated in FIG. 6, system 620includes the components of system 520 and a therapy device 650. Therapydevice 650 can be an example of therapy device 323 and can be animplantable or non-implantable device communicatively coupled toportable device via a wired or wireless communication link. Controlcircuit 322 can be implemented in portable device 542 and/or therapydevice 323. In various embodiments, system 320 is implemented in system620 as a closed-loop system for monitoring and treating at least therespiratory distress.

Example: Non-Invasive System

System 320 can be implemented as a non-invasive, minimally invasive,partially implantable, or fully implantable system. When system 320 is anon-invasive system, one or more monitoring devices 321 include one ormore non-invasive monitoring devices. In various embodiments, the one ormore non-invasive monitoring devices can include one or more passivemonitors, one or more wearable devices, one or more mobile cellulardevices, one or more adhesive patches, and/or one or more any otherforms of non-invasive monitoring devices suitable for acquiring theneeded patient condition signals.

A passive monitor (also known as in-home patient monitor, bedsidemonitor, remote patient monitor, passive patient monitor, passivein-home monitor, passive bedside monitor, etc.) can identify the patientand sense one or more signals from the identified patient. In variousembodiments, a passive monitor can use radio or microwave signals orcameras (visible or infrared) to identify individuals and detectsignals. Radio or microwave signals can be used in this manner due todifferences in wave reflection time back to the emitter, allowing forrespiratory rate, heart rate, and movement to be detected. Cameras canbe used based on minute color changes in the skin which occurs due toblood flow. Examples of physiological markers of the respiratorydistress that can be sensed using a passive monitor include respirationrate, heart rate and HRV (including frequency and time domain measures),RSA (using respiratory and cardiac signals to derive RSA metrics fromthe expiratory and inspiratory periods of physiological signals, forexample, acquired using two different filters to derive a respiratoryrate signal and a heart rate signal, or using two passive monitors),activity, movement to capture activity levels and sleep quality metrics(sleep disturbances will appear as high-amplitude spikes in the datastream), and/or blood pressure (e.g., as indicated by pulse transit timemeasure captured by a bedside camera). A passive monitor can include amicrophone to detect respiratory or lung sounds, coughing, and/or vocalexpression. A passive monitor can include an ultrasound transmitter andreceiver to detect patient movement and/or patient respiration.

A wearable monitor (also known as wearable, healthcare wearable,wearable sensor, etc.) can be worn by the patient and sense one or moresignals from the patient. Examples of wearable monitors can includewrist-worns, rings, necklaces, anklets, and sensors embedded in clothing(chest patch for example). Examples of physiological markers of therespiratory distress that can be sensed using a wearable monitor includerespiration rate (e.g., measured with accelerometers, gyroscopes,photoplethysmography (PPG) sensors, and/or impedance sensors), heartrate and heart rate variability (including frequency and time domainmeasures, e.g., measured via biopotential, bioimpedance, and/or PPGsensors), galvanic skin response (including time and frequency domainmeasures), blood pressure (including measurements such as systolic anddiastolic blood pressure, pulse transit time, wave amplitude, and/orvolume, BRS (captured by blood pressure and heart rate signals pairedwith one or more of activity, posture, and/or respiration signal),chemical marker (e.g. measured by sweat analysis), activity levels,sleep quality, vocal expression analysis, lung sounds, coughing,wheezing, and or external factors including location tracking, ambienttemperature, and/or ambient humidity.

A mobile cellular device can be worn or carried by the patient or placednear the patient and sense one or more signals from the patient. Anexample of the mobile cellular device includes a smartphone with apatient monitoring application installed. Mobile cellular devices thatallow for intermittent sensing of the patient condition signals include,for example: microphone for vocal expression analysis, accelerometer foractivity tracking, global positioning system (GPS) for location trackingand associated external factors including temperature, ambient humidity,and/or allergen levels, and/or sensors coupled to mobile devices such asECG sensors for recording heart rate, HRV (time and frequency domainmeasures), respiration rate, and RSA. Mobile cellular devices can alsobe used for continuous sensing of the patient condition signals whilethe patient is sleeping if the mobile devices are placed on mattresswhile sleeping. Examples of the patient condition parameters generatedfrom signals continuously sensed include heart rate and HRV (time andfrequency domain measures), respiration rate, RSA, and sleep qualityparameters.

An adhesive patch including one or more sensors can be attached to thepatient and sense one or more signals from the patient. Examples ofphysiological markers of the respiratory distress that can be sensedusing an adhesive patch include ECG, heart sounds, HRV, respirationrate, RSA, lung sounds, and/or electromyogram (EMG) for neuralrespiratory drive. An adhesive patch can also be made capable ofcommunicating with the user and/or insurance company to indicate when itis being worn by the patient to ensure compliance of treatmentinstructions and/or requirements.

FIG. 7 illustrates an embodiment of a system of non-invasive monitoringdevices 721 for monitoring respiratory distress. Monitoring devices 721can be an example of monitoring devices 321 and an example of usingnon-invasive (non-implantable) sensors for monitoring devices 538 insystem 520 or 620. For the purpose of illustration, but not restriction,FIG. 7 shows non-invasive monitoring devices 764, 765, 766, and 767.Monitoring device 764 can be wearable devices including sensors forsensing, for example, blood volume pulse, temperature, bodily sounds,chemical markers, and/or activity level. Monitoring device 765 can be apassive bed monitor including one or more sensors for sensing, forexample, sleep quality, hate rate, respiratory rate, and/or HRV.Monitoring device 766 can be a passive in-home monitor including one ormore radiowave sensors, ultrasound sensors, and/or cameras for sensing,for example, sleep quality, heart rate, and/or respiratory rate.Monitoring device 767 can be a bodily fluid sensor such as a salivasensor for inflammatory markers (e.g., incorporated into a toothbrushfor daily use).

In various embodiments, monitoring device(s) 321 can include one or moreminimally invasive monitoring devices. For example, monitoring device(s)321 can include sensors integrated with minimally invasive or borderlineinvasive devices such as diabetes monitor, microneedles, contact lens,tattoo, inhalable sensors, ingestible sensors, artificial limbs, and/orsensor placed in the nostril or sinus. In various embodiments,monitoring device(s) 321 can include one or more monitoring devices eachintegrated with one or more therapy devices such as nebulizer,respirator, continuous positive airway pressure (CPAP) machine, and/orchest compression devices. In various embodiments, non-invasive and/orminimally invasive monitoring devices, when used individually or incombination, can provide a system for the patients to track symptomsobjectively over time, to identify when the patient's condition isdeteriorating, and to provide information to the patient and/or the userwhen appropriate. These monitoring devices can also provide inputs to aclosed-loop therapy system.

Example: Monitoring Respiration-Mediated Parameter

In various embodiments, the state of the respiratory distress can bemonitored using one or more respiration-mediated physiologicalparameters, such as one or more measures of respiratory sinus arrhythmia(RSA), that are indicative of the patient's autonomic balance. The stateof the respiratory distress can be measured by a degree of autonomicimbalance. FIG. 8 illustrates an example of short-term heart ratevariability (HRV) throughout a respiration cycle under healthy anddiseased conditions. FIG. 9 illustrates another example of short-termHRV throughout a respiration cycle under healthy and diseasedconditions. RSA is characterized by the magnitude of HRV at differenttime points along the respiration cycle and/or ratios between HRV atvarious time points along the respiration cycle. In various embodiments,the one or more patient condition parameters and/or metrics can includeone or more measures of the RSA for determine the state of therespiratory distress, such as asthma or COPD.

Respiratory distress monitoring circuit 100, 200, or 300 can beconfigured to monitor the state of the respiratory distress using one ormore respiration-mediated physiological parameters, such as one or moremeasures of RSA. Referring back to FIG. 1, Signal inputs 101 can receiveone or more respiratory signals and one or more cardiac signals. The oneor more respiratory signals are indicative of respiratory cyclesincluding inspiratory and expiratory phases. The one or more cardiacsignals are indicative of cardiac cycles including at least ventriculardepolarizations (R-waves). Signal processing circuit 102 can beconfigured to process the one or more respiratory signals and the one ormore cardiac signals and to generate one or more respiration-mediatedphysiological parameters indicative of the state of the respiratorydistress, such as one or more RSA parameters being one or more measuresof the RSA. Respiratory distress analyzer 103 can be configured todetermine the state of the respiratory distress using the one or morerespiration-mediated physiological parameters. Physiological markerinput 106 can receive the one or more respiration-mediated physiologicalparameters. Parameter analysis circuit 105 can be configured to analyzethe one or more respiration-mediated physiological parameters receivedfrom signal processing circuit 102 and determine the state of therespiratory distress using an outcome of the analysis.

Referring back to FIG. 2, signal inputs 201 can be configured to includea respiratory signal input to receive the one or more respiratorysignals and a cardiac signal input to receive the one or more cardiacsignals. Signal processing circuit 202 can be to process the one or morerespiratory signals and the one or more cardiac signals and to generatethe patient condition parameters indicative of the state of therespiratory distress. The patient condition parameters include the oneor more respiration-mediated physiological parameters indicative of thestate of the respiratory distress, such as one or more RSA parameterseach being a measure of the RSA. Respiratory distress analyzer 203 canbe configured to determine the state of the respiratory distress basedon the one or more respiration-mediated physiological parameters.Physiological parameter input 206 can be configured to receive the oneor more respiration-mediated physiological parameters. The one or morerespiration-mediated physiological parameters can include, but are notlimited to, one or more of the following parameters:

-   -   (a) Absolute heart rate, HRV, or R-R interval during inspiration        and expiration;    -   (b) Change in heart rate, HRV, or R-R interval over respiration        cycle;    -   (c) Ratio of heart rate, HRV or R-R interval during expiration        to inspiration;    -   (d) Each in (a)-(c) above averaged over time (e.g., an ensemble        average over multiple respiratory cycles);    -   (e) Measure of deviation from normal respiration heart rate        cycling (e.g., heart rate or R-R interval plotted as a function        of respiration phase);    -   (f) Frequency-domain parameters of heart rate and HRV as        functions of respiration; and/or    -   (g) Phase of the respiratory signal and corresponding cardiac        signal.

Parameter analysis circuit 205 can be configured to determine the stateof the respiratory distress based on at least the one or morerespiration-mediated physiological parameters. In one embodiment,parameter analysis circuit 205 can be configured to produce arespiration-mediated signal metric including a linear or nonlinearcombination of the respiration-mediated physiological parameters and toproduce the one or more respiratory distress indicators indicating thestate of the respiratory distress based on the respiration-mediatedsignal metric. In various embodiments, respiratory distress monitoringcircuit 300 can be configured to monitor the state of the respiratorydistress using the one or more respiration-mediated physiologicalparameters in combination with any one or more other physiologicalmarker parameters, and/or other parameters related to the respiratorydisorder, that are discussed in this document.

Referring back to FIG. 3, when respiratory distress monitoring circuit300 is configured to monitor the state of the respiratory distress usingthe one or more respiration-mediated physiological parameters, such asthe one or more measures of RSA, monitoring device(s) 321 can includeone or more sensors that can be configured to sense the one or morerespiratory signals and the one or more cardiac signals. In variousembodiments, monitoring device(s) 321 can include an implantable,injectable, non-invasive, wearable, or passive monitoring device, or acombination of any of these devices, including one or more sensors toacquire, for example, a signal corresponding to respiration to indicateperiod of inspiration and expiration and a signal corresponding to heartrate to evaluate changes in R-R intervals during periods of therespiratory cycle identified by the respiration signal. These signalsallow parameter analysis circuit 205 to produce a metric ofrespiration-mediated physiological signal. In various embodiments, theone or more respiration signals can be acquired directly or indirectlyusing, for example, one or more of the following:

-   -   (a) Respiration sensor (e.g., patient-contacting sensor such as        chest and abdominal movement sensor, acoustic sensor, airflow        sensor, muscle strain sensor, and/or impedance sensor, and/or        non-contacting sensor such as radio or microwave based tissue        movement sensor, optical sensor, acoustic sensor, camera, and/or        accelerometer or gyroscope);    -   (b) ECG sensor (for deriving periods of inspiration and        expiration from ECG, which is modulated by respiratory        activity);    -   (c) Heart sound sensor (for deriving periods of inspiration and        expiration from a heart sound signal that is modulated by        respiratory activity); and/or    -   (d) Blood pressure sensor (e.g., PPG sensor, blood pressure        cuff, and/or invasive blood pressure sensor).        The one or more cardiac signals (or surrogate        respiration-mediated signals) can be acquired using, for        example, one or more of the following:    -   (a) ECG sensor (ECG is modulated by respiratory activity);    -   (b) Heart sound sensor (acoustic vibrations from the cardiac        cycle are modulated by respiratory activity);    -   (c) Blood pressure or flow sensor (e.g., PPG sensor, blood        pressure cuff, and/or invasive blood pressure sensor);    -   (d) Blood gas concentration sensor (e.g., pulse oximeter and/or        invasive blood gas sensor; and/or    -   (e) Nerve sensor for direct neural recordings (e.g., surface        electrodes and/or invasive nerve recording sensors).        In various embodiments, monitoring device(s) 321 can include one        or more sensors in one or more remote devices coupled to        respiratory distress monitoring circuit 300 via one or more        wireless or wired communication links. Such one or more remote        devices can include, but are not limited to, one or more of the        following:    -   (a) Invasive or noninvasive device for processing sensor input        data;    -   (b) Personal device for alerts and notification on pain levels;        and/or    -   (c) Invasive or noninvasive devices used as part of a        closed-loop system, including a closed-loop systems where the        patient closes the loop by himself/herself.

FIG. 10 illustrates an embodiment of a method 1070 for monitoringrespiratory distress based on a patient condition metric derived fromrespiratory and cardiac signals, such as an RSA metric. Method 1070 canbe performed using system 320, which can be implemented in system 520 or620.

At 1071, patient condition signals are received. The patient conditionsignals include a respiratory signal indicative of inspiration andexpiration times and a cardiac signal indicative of R-R interval (i.e.,as referred to as cardiac cycle length or ventricular rate interval,measure as the time interval between consecutive R-waves). At 1072, thepatient condition metric (e.g., the RSA metric) is determined using therespiratory and cardiac signals. At 1073, the state of the respiratorydistress is determined based the patient condition metric such as theRSA metric. If the state of the respiratory distress (e.g., aquantitative measure of the state) does not exceed a threshold at 1074,method 1070 continues from 1071 again. If the state of the respiratorydistress exceeds the threshold at 1074, an alert is produced to notifythe patient and/or the user, and/or one or more therapies treating therespiratory distress are delivered, at 1075. Method 1070 can continuefrom 1071 again to monitor the state of the respiratory distressincluding the effect of the delivery of the one or more therapies and/orother medical intervention resulting from the alert.

FIG. 11 illustrates an embodiment of a method for monitoring RSA usingECG and accelerometer signals. In one embodiment, the ECG andaccelerometer signals are sensed using a chest patch attached to thechest of the patient. In another embodiment, the ECG and accelerometersignals are sensed using an implantable cardiac monitor (ICM) that isplaced within the patient. Mean heart rates are calculated using R-wavesdetected from the ECG signal separately for inspiration and expirationperiods detected from the accelerometer signal. An RSA index(representing an autonomic measure) is calculated as a ratio of the meanheart rate during inspiration to the mean heart rate during expiration.FIG. 12 illustrates an example of RSA index plotted against time forhealthy and diseased (with the respiratory distress. An exacerbation ofthe respiratory distress is detected or predicted when the RSA indexfalls below a threshold (dash line). In various embodiments, differentthresholds can be used for detection and prediction. An alert can beproduced to notify the patient and/or the user that exacerbation of therespiratory distress is detected. A distinct alert can be produced tonotify the patient and/or the user that exacerbation of the respiratorydistress is predicted.

Example: Monitoring Brs

In various embodiments, the state of the respiratory distress can bemonitored using one or more respiration-mediated physiologicalparameters, such as one or more measures of baroreflex sensitivity (BRS)that are indicative of the patient's autonomic balance. The state of therespiratory distress can be measured by a degree of autonomic imbalance.FIG. 13 illustrates an example of BRS under healthy and diseasedconditions. With abnormal autonomic activity associated with therespiratory distress, the change in heart rate in response to change inblood pressure is attenuated, resulting in decreased BRS. Thisattenuation in BRS can lead to impaired sympathetic inhibition, elevatedblood pressure, and worsening of the respiratory distress. In variousembodiments, the one or more patient condition parameters and/or metricscan include one or more measures of the BRS for determine the state ofthe respiratory distress, such as asthma or COPD.

Respiratory distress monitoring circuit 100, 200, or 300 can beconfigured to monitor the state of the respiratory distress using one ormore BRS parameters being one or more measures of BRS. Referring back toFIG. 1, Signal inputs 101 can receive one or more blood pressuresignals, one or more cardiac signals, and one or more physical statesignals. The one or more respiratory signals are indicative ofrespiratory cycles including inspiratory and expiratory phases. The oneor more cardiac signals are indicative of cardiac cycles including atleast ventricular depolarizations (R-waves). The one or more physicalstate signals each indicate a physical state or change in the physicalstate of the patent that affects the patient's BRS. Signal processingcircuit 102 can be configured to process the one or more blood pressuresignals, the one or more cardiac signals, and the one or more physicalstate signals and to generate the one or more BRS parameters.Respiratory distress analyzer 103 can be configured to determine thestate of the respiratory distress using the one or more BRS parameters.Physiological marker input 106 can receive the one or more BRSparameters. Parameter analysis circuit 105 can be configured to analyzethe one or more BRS parameters received from signal processing circuit102 and determine the state of the respiratory distress using an outcomeof the analysis.

Referring back to FIG. 2, signal inputs 201 can be configured to includea blood pressure input to receive the one or more blood pressuresignals, a cardiac signal input to receive the one or more cardiacsignals, and a physical state input to receive the one or more physicalstate signals. Signal processing circuit 202 can be configured toprocess the one or more blood pressure signals, the one or more cardiacsignals, and the one or more physical state input signals and togenerate the patient condition parameters indicative of the state of therespiratory distress. The patient condition parameters include the oneor more BRS parameters and one or more physical state parameters.Respiratory distress analyzer 203 can be configured to determine thestate of the respiratory distress based on the one or more BRSparameters and the one or more physical state parameters. The one ormore physical state parameters indicate one or more physical states ofthe patient that affects the patient's BRS and allow the one or more BRSparameters to be expressed as functions of the one or more physicalstates. Physiological parameter input 206 can be configured to receivethe one or more BRS parameters and the one or more physical stateparameters. The one or more BRS parameters can include, but are notlimited to, one or more of the following parameters:

-   -   (a) BRS (which can vary based on minimum blood pressure and        heart rate thresholds for minimum change, and can vary based on        the minimum number of beats in a sequence);    -   (b) Range of BRS;    -   (c) Coherence or correlation measures;    -   (d) Delay or latency;    -   (e) Recovery times;    -   (f) Baroreceptor characterization—sigmoid curve and morphology;    -   (g) Change in cardiac measure (e.g., captured as a slope of        change or as a time interval for the parameter to reach a        certain percentage of the peak change);    -   (h) Change in blood pressure measure (e.g., captured as a slope        of change or as a time interval for the parameter to reach a        certain percentage of the peak change); and/or    -   (i) Other measure(s) of baroreceptor response.

The one or more physical state parameters can include, but are notlimited to, one or more of the following parameters:

-   -   (a) Respiratory cycle timing (timing of inspiration and        expiration phases);    -   (b) Level of physical activity or exertion (e.g., indicated by        the activity and respiration sensors, classified as mild,        moderate, or vigorous activity)(baroreceptor response can be        characterized over a continuum of levels of physical activity or        exertion indicated by signals, such as activity, respiration,        and/or biochemical markers, for example, by vector magnitude        units (in g) over a period of time, caloric expenditure,        distance traveled, or other activity or exertion measures, or a        combination of these parameters);    -   (c) Type of posture change (e.g., indicated by the posture        sensor, classified as laying to sitting, laying to standing,        sitting to standing, etc.); and/or    -   (d) Magnitude of posture change (angle) and/or time duration of        posture change (seconds, or degrees/second).        The one or more BRS parameters can be stratified by values of        such one or more physical state parameters.

Parameter analysis circuit 205 can be configured to determine the stateof the respiratory distress based on at least the one or more BRSparameters and the one or more physical state parameters. In oneembodiment, parameter analysis circuit 205 can be configured to producea BRS metric including a linear or nonlinear combination of the one ormore BRS parameters as stratified by the one or more physical stateparameters and to produce the one or more respiratory distressindicators indicating the state of the respiratory distress based on theBRS metric. In various embodiments, respiratory distress monitoringcircuit 300 can be configured to monitor the state of the respiratorydistress using the one or more BRS parameters and the one or morephysical state parameters in combination with any one or more otherphysiological marker parameters, and/or other parameters related to therespiratory disorder, that are discussed in this document.

Referring back to FIG. 3, when respiratory distress monitoring circuit300 is configured to monitor the state of the respiratory distress usingthe one or more BRS parameters and the one or more physical stateparameters, monitoring device(s) 321 can include one or more sensorsthat can be configured to sense the one or more blood pressure signals,the one or more cardiac signals, and the one or more physical statesignals. In various embodiments, monitoring device(s) 321 can include animplantable, injectable, non-invasive, wearable, or passive monitoringdevice, or a combination of any of these devices, including one or moresensors to acquire, for example, one or more signals indicative ofbaroreceptor response and one or more signals indicative of activitylevel, respiration, and/or posture of the patient. In variousembodiments, the one or more sensors can include, but are not limitedto, one or more of the following:

-   -   (a) A sensor to directly or indirectly sense a blood pressure        signal (e.g., a pressure sensor to sense the blood pressure        directly through invasive or noninvasive means; a heart sound        sensor to sense the second heard sound (S2), a sensor to sense        pulse transit time, and/or a sensor to sense a blood volume        pulse waveform;    -   (b) A sensor to directly or indirectly sense a cardiac signal        (e.g., ECG allowing for measuring heart rate or R-R interval        and/or HRV, including time and/or frequency domain measures of        the HRV, and/or a heart sound signal allowing for detection of        the first heart sound (S1)); and/or    -   (c) One or more sensors to sense the physical state of the        patient (e.g., activity, posture, respiration rate, and heart        rate):        -   (i) An activity sensor including one or more of            accelerometers, gyroscopes, electromyography (EMG) sensors,            GPS, or other sensors indicating physical activity;        -   (ii) A posture sensor including one or more of            accelerometers, gyroscopes, passive motion capture, or other            sensors indicating postures; and/or        -   (iii) A sensor to directly or indirectly sense the            respiration rate and/or the heart rate, such as one or more            of:            -   (1) Respiration sensor (e.g., patient-contacting sensor                such as chest and abdominal movement sensor, acoustic                sensor, airflow sensor, muscle strain sensor, and/or                impedance sensor, and/or non-contacting sensor such as                radio or microwave based tissue movement sensor, optical                sensor, acoustic sensor, camera, and/or accelerometer or                gyroscope);            -   (2) ECG sensor (for deriving periods of inspiration and                expiration from ECG, which is modulated by respiratory                activity);            -   (3) Heart sound sensor (for deriving periods of                inspiration and expiration from a heart sound signal                that is modulated by respiratory activity); and/or            -   (4) Blood pressure sensor (e.g., PPG sensor, blood                pressure cuff, and/or invasive blood pressure sensor).                In various embodiments, monitoring device(s) 321 can                include one or more sensors in one or more remote                devices coupled to respiratory distress monitoring                circuit 300 via one or more wireless or wired                communication links. Such one or more remote devices can                include, but are not limited to, one or more of the                following:    -   (a) Invasive or noninvasive device for processing sensor input        data;    -   (b) Personal device for alerts and notification on pain levels;        and/or    -   (c) Invasive or noninvasive devices used as part of a        closed-loop system, including a closed-loop systems where the        patient closes the loop by himself/herself.

FIG. 14 illustrates an embodiment of a method 1480 for monitoringrespiratory distress based on a BRS metric. Method 1480 can be performedusing system 320, which can be implemented in system 520 or 620.

At 1481, patient condition signals are received. The patient conditionsignals include a respiratory signal indicative of inspiration andexpiration times, a cardiac signal indicative of R-R interval, and ablood pressure signal indicative systolic blood pressure. At 1482, theBRS metric is determined using the respiratory, cardiac, and bloodpressure signals. At 1483, the state of the respiratory distress isdetermined based the BRS metric. If the state of the respiratorydistress (e.g., a quantitative measure of the state) does not exceed athreshold at 1484, method 1480 continues from 1481 again. If the stateof the respiratory distress exceeds the threshold at 1484, an alert isproduced to notify the patient and/or the user, and/or one or moretherapies treating the respiratory distress are delivered, at 1485.Method 1480 can continue from 1481 again to monitor the state of therespiratory distress including the effect of the delivery of the one ormore therapies and/or other medical intervention resulting from thealert.

FIG. 15 illustrates an example of a method for monitoring BRS using ECG,blood pressure, and accelerometer signals sensed by a chest patch and awrist-worn device on the patient. FIG. 16 illustrates an example of amethod for monitoring BRS using ECG, heart sound, and accelerometersignals sensed by an ICM. The accelerometer signal is used as arespiratory signal indicative of inspiration and expiration phases usesas the patient's physical state. A BRS index (representing an autonomicmeasure) is calculated as a slope of a curve being the R-R intervalagainst the systolic blood pressure (as indicated by the heart sounds).This represents one of various techniques to quantify spontaneous BRS,and allows for “up” and “down” sequences, which are controlled bydifferent mechanisms, to be evaluated separately. Other techniquesinclude spectral methods that look at the power of the blood pressureand heart rate signals in certain frequency ranges as well as theirratios. FIG. 17 illustrates an example of BRS index plotted against timefor healthy and diseased (with the respiratory distress). Anexacerbation of the respiratory distress is detected or predicted whenthe BRS index falls below a threshold (dash line). In variousembodiments, different thresholds can be used for detection andprediction. An alert can be produced to notify the patient and/or theuser that exacerbation of the respiratory distress is detected. Adistinct alert can be produced to notify the patient and/or the userthat exacerbation of the respiratory distress is predicted.

It is to be understood that the above detailed description is intendedto be illustrative, and not restrictive. Other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the invention should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

TABLE 1 Physiological markers for the respiratory distress. Physiology/Response to Exemplary Method of Measurement Additional Notes AcuteExacerbation Measurement/Devices Respiratory Rate Dyspnea. IncreasedIncrease Impedance measure, respiratory rate and/or accelerometer, EMG,decreased tidal volume radio/micro waves, optical-based sensor,acoustic-based sensor, camera, ECG Heart Rate Increased heart rate.Increase ECG, heart sounds, Overall increase in accelerometer,sympathetic activation and radio/micro waves, a decrease inoptical-based sensor, parasympathetic acoustic-based activation sensor,camera Cough Increased occurrence of Increase Microphone, coughingleading up to accelerometer exacerbation Wheezing More frequent wheezingIncrease Microphone, accelerometer Oxygen Saturation Blood O2 saturationDecrease Pulse oximeter; Smartphone app Central Cyanosis Blood O2saturation Increase Pulse oximeter; Smartphone app Altered Balance andposture can Variable Accelerometer Consciousness be altered due to theonset (gradual or sudden) of an exacerbation Activity levels Decrease inactivity levels Risk factor/ Accelerometer due to labored breathingTrigger Sleep quality Poor sleep quality, more Decrease Accelerometer,often awakening, sleeping gyroscope, ECG, in an upright positionradio/micro waves, optical-based sensor, acoustic-based sensor, camera,GSR Posture/Chest Poorer posture overall, Variable Accelerometer,posture chest inflation alters chest gyroscope (posture) posture BalanceReduced balance, Decrease Accelerometer, coordination gyroscope(posture) Gait Altered gait pattern Variable Accelerometer VocalPatients with acute, severe Turbulent, Microphone Expression asthmaappear seriously altered dyspneic at rest, are unable to talk withsentences or phrases Inflammation Increase in inflammatory IncreaseChemical sensor markers (blood, saliva, breath, sputum, etc.) AccessoryRapid, shallow breathing Variable EMG Muscle Activity changesabdominal/thoracic muscle activity Physical Stress Factors intopatient's Risk factor/ Subjective input overall health and Triggersusceptibility to infection or other triggering event Mental StressFactors into patient's Risk factor/ Subjective input overall health andTrigger susceptibility to infection or other triggering event MenstrualCycle Factors into patient's Risk factor/ Subjective input overallhealth and Trigger susceptibility to infection or other triggering eventTime-of- Factors into patient's Risk factor/ Automatically day/yearoverall health and Trigger Synced (GPS, mobile susceptibility toinfection device) or other triggering event Mucus Elevated mucusIncrease Impedance measure production production blocks arieays,restricting airflow and difficulty breathing Airway smooth Airwaysconstrict making Increase Impedance measure, muscle contraction itdifficult to breath EMG Autonomic Function Heart Rate Decrease in heartrate Decrease ECG, heart sounds, Variability variability due to theaccelerometer, imbalance in the radio/micro waves, autonomic nervousoptical-based sensor. system, with sympathetic acoustic-based systemdominating. sensor, camera Respiration Essentially the transfer DecreaseECG, heart sounds, Sinus function from respiration accelerometer,Arrhythmia rate to R-R intervals. radio/micro waves, Another way toassess optical-based sensor, cardiac autonomic acoustic-based function.RSA which sensor, camera decreases in the presence of increasedsympathetic activity/decreased parasympathetic activity. Heart Rate Needpremature Decrease ECG, heart sounds Turbulence ventricular complexes tobe occurring to quantify HRT Baroreceptor Measured after injectionDecrease ECG, PPG, heart Reflex of phenylephrine sounds Sensitivity Alsoa spontaneous measures that can be acquired continuously usingrespiration as a way to alter autonomic balance Heart Rate Sympatheticautonomic Increase ECG, heart sounds, Acceleration nerves act to quickenthe accelerometer Capacity heart and strengthen the accelerationcapacity. During AECOPD, airflow obstruction aggravates autonomicfunction, resulting in an imbalance in the system - increase insympathetic activity Heart Rate Vagal nerve slows the Decrease ECG,heart sounds, Deceleration heart rate and enhances accelerometerCapacity the heart rate deceleration capacity. During AECOPD, airflowobstruction aggravates autonomic function, resulting in an imbalance inthe system - decrease in parasympathetic (vagal) activity. Galvanic SkinAcute stress, anxiety Increase Electrodes on the Response/ caused by anexacerbation hand Electrodermal results in increased activitysympathetic activity which causes sweat glands to fill up and skinconductance increases creating skin conductance fluctuations. BloodPressure increase in blood pressure Increase PPG, S2, Pulse due toincreased amplitude sympathetic nervous system activity and resultingvasoconstruction Blood Flow Diaphragmatic blood flow Decrease PPG, S2,Pulse reduces during acute amplitude episodes. In the case ofpersistence of the severe asthma attack, ventilatory muscles cannotsustain adequate tidal volumes and respiratory failure ensues. PerfusionDiaphragmatic perfusion Decrease PPG reduces during acute episodes. Inthe case of persistence of the severe asthma attack, ventilatory musclescannot sustain adequate tidal volumes and respiratory failure ensues.Skin Variable Thermometer Temperature Body Bacterial or viral IncreaseThermometer Temperature infection, may cause your body temperature torise Pupil Diameter Dilation of the pupil is Increase Camera indicativeof sympathetic activation Electrooc- Correlates to autonomic VariableElectrodes ulography tone - variable relationship depending ontime/frequency domain analysis performed Pulse Transit Increasedsympathetic Decrease PPG Time & Pulse activity constricts Wave Amplitudevasculature causing transit (Alternative time and wave amplitude measurefor BP) to decrease Normalized Sympathetic tone causes Decrease PPGPulse Volume vascular construction. (NPV) NPV can be derived from fingertip PPG and also from the bottom of the ear canal. NPV is an indirectmeasures of autonomic tone Forced Expiratory Volume FEV1 forciblyexhaled air in 1 Decreased Thoracic impedance, second; mainly reflects(decreases accelerometers, flow larger airways obstruction withstage/severity) sensors, ECG FEV1/FVC fixed ratio <70% defines DecreaseThoracic impedance, airflow limitations. FVC = accelerometers, flowforced vital capacity sensors, ECG TLC Total lung capacity is theIncrease Thoracic impedance, greatest volume of gas in accelerometers,flow the lungs after maximal sensors, ECG voluntary inspiration.Increase in TLC in COPD usually reflects lung compliance due toemphysema, as thoracic compliance decreases FRC Functional residualIncrease Thoracic impedance, capacity is the lung accelerometers, flowvolume at the end of quiet sensors, ECG expiration during tidalbreathing. Increased in COPD patients. FEV3 later fraction of forcedDecrease Thoracic impedance, exhalation better reflects accelerometers,flow smaller airway sensors, ECG contributions and may be a moresensitive measure to diagnose early airway obstruction in COPD FEV3/FEV6ratio of later fraction Decrease Thoracic impedance, measures of forcedaccelerometers, flow exhalation to represent the sensors, ECG smallairways. Ratio less than the lower limit of normal as the soleabnormality identifies a distinct population with evidence of smallairways disease advantage of spirometric ratios is that they have lessvariability than do timed forced expirations Lung Absolute lung volumeis TLC, FRC, &RV Thoracic impedance, hyperinflation: evaluated bymeasuring all >= 120-130% accelerometers, flow TLC, FRC, RV the increasein total lung sensors, ECG capacity (TLC), functional residual capacity(FRC), residual volume (RV), and decrease in inspiratory capacity (IC).Lung hyperinflation exists when TLC, FRC, and RV >= 120-130% of thepredicted volume Tidal Volume increase in displaced air IncreaseThoracic impedance, (VT) between inspiration and accelerometers, flowexpiration. Short rapid sensors, ECG breathing Peak expiratory Maximumspeed of Decrease Thoracic impedance, flow (PEF) expiration decreases asthe accelerometers, flow airways become sensors, ECG blocked/constrictedForced Amount of air that can be Decrease Thoracic impedance, expiratoryexhaled decreases. accelerometers, flow volume (FEV) Expiration becomessensors, ECG slower and more difficult Inspiration/ Normal is 1:2 atrest, 1:1 Decrease Thoracic impedance, expiration during exercise. Ratioaccelerometers, flow ratio (IER) decrease with an sensors, ECGincreasing expiration period due to difficulty breathing, expelling airfrom the lungs Minute volume Total volume of gas Increase Thoracicimpedance, (MV) inhaled or exhaled in 1 accelerometers, flow minute.Rapid breathing sensors, ECG during exacerbation Forced vital Amount ofair (total Decrease Thoracic impedance, capacity (FVC) amount of air)exhaled accelerometers, flow during the FEV test. sensors, ECG EndExpiratory Corresponds to FRC in the Increase Thoracic impedance, volume(EEV)/ presence of positive end accelerometers, flow ΔrEEV expirationpressure. sensors, ECG ΔrEEV is used if short term filtering is used.HII Hyperinflation causes the Increase Thoracic impedance,(hyperinflation patient to operate on the accelerometers, flow index)relatively flat portion of sensors, ECG the chest wall-lung compliancecurve leading to a rapid shallow breathing pattern Airway ResistanceImpulse Pressure oscillations are Variable Impulse oscillometryOscillometry applied at the mouth to system (IOS) (IOS) measurepulmonary resistance and reactance. Noninvasive, rapid techniquerequiring only passive cooperation by the patient. Neural MeasuresNeural Calculated as the product Increase EMG device Respiratory of thesecond intercostal Drive Index space parasternal (NRDI) electromyographyactivity normalized to the peak EMG activity during a maximuminspiratory sniff manoeuver. Parasternal EMG (EMGpara) signals recordedfrom surface electrodes have a direct relationship with respiratorymuscle load and have been shown to respond to acute change.Differentiates between “improvers” and “deteriorators” in the hospital,and was also a predictor of hospital readmittance. Diaphragmatic Knownmechanisms of Variable Changes due to compromised diaphragmaticHyperinflation function secondary to * Not currently hyperinflation:measured but worsening of the length- secondary tension relationshipeffects due to decrease in the zone of hyperinflation apposition couldbe decrease in the curvature captured change in the mechanicalarrangement of costal and crural components increase in the elasticrecoil of the thoracic cage Exhaled Breath Exhaled Breath Exhaled breathtemperature Increase --> eNose, SpiroNose Temperature can be anindication of airway AECOPD (breathcloud.org), inflammation. PeakDecrease --> chemical sensor exhaled breath in patients stable COPD withexacerbations increased and dropped down with recovery. Patients withstable COPD had decreased peak EBT in comparison to controls (nonsmokers and smokers) Fractional During inflammation, Increase eNose,chemical exhaled nitric larger amount of NO are sensor oxide (FeNO)produced for prolonged periods. NO concentrations are known to be higherin disease such as asthma and COPD. pH - expired Acidification (decreasein Decrease eNose, chemical breath pH) could be a maker of sensor airwayinflammation and disease severity. pH is reduced during acuteexacerbations O2 - expired Rapid shallow breathing, Increase eNose,chemical breath increases respiratory O2 sensor concentrations. ReducesCO2 concentrations CO2 - expired In early stages of acute DecreaseeNose, chemical breath exacerbations, patients sensor have respiratoryalkalosis Volatile Organic Electronic noses that can Compounds pick upVOCs to assess (VOCs) profile and classify patients Inflammatory relatedand detectable in exhaled breath 13 VOCs: Isoprene, C16 PredictiveeNose, chemical hydrocarbon, 4,7- Profile sensor Dimethyl-undecane, 2,6-Dimethyl-heptane, 4- Methyl-octane, Hexadecane, 3,7- Dimethyl1,3,6-octane, 2,4,6-Trimethyl-decane, Hexanal, Benzonitrile, Octadecane,Undecane, Terpineol Chemical Markers Nitric Oxide During inflammation,Increase Chemical Sensor larger amount of NO are produced for prolongedperiods. NO concentrations are known to be higher in disease such asasthma and COPD. CRP Nonspecific marker of Increase Chemical Sensorinflammation, has an inverse relation with lung function and probablyreflects disease severity. CRP levels rise during exacerbationsparticularly when there is an increased neutrophilic influx due toabacterial cause. Also, a raised CRP in stable state predicts recurrentexacerbations either due to a failure to completely resolve the firstepisode or an underlying airway colonization that predisposes to furtherepisodes External Index Air Temperature Cold temperatures Riskfactor/Trigger Integrated weather increase risk of application to syncexacerbation with devices to Air Increase in known Risk factor/Triggershown when someone Contaminants allergens for patients Increase inparticulate is more at risk increases risk of an count/size --> Ambientair sensor exacerbation increase in AECOPD Real-time monitoring ofpersonal air pollution exposure Can monitor number of particulates andtheir size- can inform predictions of acute exacerbations or have morelong-term monitoring benefits Humidity cold dry air is a trigger forRisk factor/Trigger asthma attacked Altitude Fewer exacerbations occurRisk factor/Trigger at high altitudes Air Pressure Fewer exacerbationsoccur Risk factor/Trigger at low pressure

What is claimed is:
 1. A system for monitoring and treating respiratorydistress in a patient, comprising: a signal input configured to receivepatient condition signals indicative of autonomic balance of thepatient; a signal processing circuit configured to process the patientcondition signals and to generate patient condition parameters using theprocessed patient condition signals, the patient condition parametersindicative of the autonomic balance of the patient; and a respiratorydistress analyzer configured to determine a state of the respiratorydistress using the patient condition parameters, the respiratorydistress analyzer including a parameter analysis circuit configured toanalyze the autonomic balance of the patient and to determine the stateof the respiratory distress using an outcome of the analysis.
 2. Thesystem of claim 1, further comprising: a therapy device configured todeliver one or more therapies treating the respiratory distress; and acontrol circuit configured to control the delivery of the one or moretherapies based on the state of the respiratory distress.
 3. The systemof claim 1, wherein the parameter analysis circuit is configured todetermine a patient condition metric being a linear or nonlinearcombination of the patient condition parameters and to perform at leastone of prediction or detection of an exacerbation of the respiratorydistress based on the patient condition metric, and the respiratorydistress analyzer further comprises a notification circuit configured toproduce an alert notifying a result of the performance of the at leastone of prediction and detection.
 4. The system of claim 3, wherein thesignal processing circuit is configured to generate patient conditionparameters indicative of one or more physiological markers of asthma,the parameter analysis circuit is configured to perform at least one ofprediction or detection of an asthma attack, and the notificationcircuit is configured to produce an asthma alert notifying at least oneof the asthma attack being predicted or the asthma attack beingdetected.
 5. The system of claim 3, wherein the signal processingcircuit is configured to generate patient condition parametersindicative of one or more physiological markers of chronic obstructivepulmonary disease (COPD), the parameter analysis circuit is configuredto perform at least one of prediction or detection of an exacerbation ofCOPD, and the notification circuit is configured to produce a COPD alertnotifying at least one of the exacerbation of COPD being predicted orthe exacerbation of COPD being detected.
 6. The system of claim 1,further comprising: a signal processing controller configured to receivea processing control signal and adjust the processing of the patientcondition signals based on the processing control signal; and a signalprocessing sensor configured to sense a physical state of the patientand to produce the processing control signal based on the physicalstate.
 7. The system of claim 1, wherein the signal input are configuredto receive one or more respiratory signals indicative of respiratorycycles including inspiratory and expiratory phases and one or morecardiac signals indicative of cardiac cycles including at leastventricular depolarizations, the signal processing circuit is configuredto process the one or more respiratory signals and the one or morecardiac signals and to generate one or more respiration-mediatedphysiological parameters of the patient condition parameters, and theparameter analysis circuit is configured to determine the state of therespiratory distress based on at least the one or morerespiration-mediated physiological parameters.
 8. The system of claim 7,wherein the signal processing circuit is configured to generate one ormore respiration sinus arrhythmia (RSA) parameters of the one or morerespiration-mediated physiological parameters, the one or more RSAparameters being one or more measures of the RSA, and the parameteranalysis circuit is configured to determine the state of the respiratorydistress based on at least the one or more RSA parameters.
 9. The systemof claim 1, wherein the signal input are configured to receive one ormore blood pressure signals indicative of blood pressure, one or morecardiac signals indicative of cardiac cycles including at leastventricular depolarizations, and one or more physical state signalsindicative of a physical state of the patient, the signal processingcircuit is configured to process the one or more blood pressure signals,the one or more cardiac signals, and the one or more physical statesignals and to generate one or more baroreflex sensitivity (BRS)parameters of the patient condition parameters, the one or more BRSparameters being one or more measures of the BRS, and the parameteranalysis circuit is configured to determine the state of the respiratorydistress based on at least the one or more BRS parameters.
 10. Thesystem of claim 9, wherein the signal processing circuit is configuredto detect levels of physical activity or exertion of the patient fromthe one or more physical state signals and to generate the one or moreBRS parameters each for a plurality of levels of the physical activityor exertion.
 11. The system of claim 9, wherein the signal processingcircuit is configured to detect a type of posture change of the patientfrom the one or more physical state signals and to stratify the one ormore BRS parameters by the detected type of posture change.
 12. Thesystem of claim 11, wherein the signal processing circuit is configuredto detect one or more of a magnitude or a duration of posture change ofthe patient from the one or more physical state signals and to stratifythe one or more BRS parameters by the detected one or more of themagnitude or the duration of posture change.
 13. A method for monitoringand treating respiratory distress in a patient, comprising: receivingpatient condition signals indicative of autonomic balance of thepatient; and monitoring the state of the respiratory distressautomatically using a respiratory distress monitoring circuit, themonitoring including: processing the patient condition signals;generating patient condition parameters using the processed patientcondition signals, the patient condition parameters indicative of theautonomic balance of the patient; analyzing the autonomic balance of thepatient using the patient condition parameters; and determining thestate of the respiratory distress using an outcome of the analysis. 14.The method of claim 13, further comprising: delivering one or moretherapies treating the respiratory distress; and controlling thedelivery of the one or more therapies based on the state of therespiratory distress.
 15. The method of claim 13, further comprising:determining a patient condition metric being a linear or nonlinearcombination of the patient condition parameters; performing at least oneof prediction or detection of an exacerbation of the respiratorydistress based on the patient condition metric; and producing an alertnotifying a result of the performance of the at least one of predictionand detection.
 16. The method of claim 13, further comprising: sensing aphysical state of the patient; and adjusting the processing of thepatient condition signals based on the sensed physical state.
 17. Themethod of claim 13, wherein the receiving patient condition signalscomprises receiving one or more respiratory signals indicative ofrespiratory cycles including inspiratory and expiratory phases and oneor more cardiac signals indicative of cardiac cycles including at leastventricular depolarizations, and generating the patient conditionparameters comprises generating one or more respiration-mediatedphysiological parameters of the patient condition parameters.
 18. Themethod of claim 17, wherein generating the one or morerespiration-mediated physiological parameters comprises generating oneor more respiration sinus arrhythmia (RSA) parameters being one or moremeasures of the RSA.
 19. The method of claim 13, wherein the receivingpatient condition signals comprises receiving one or more blood pressuresignals indicative of blood pressure, one or more cardiac signalsindicative of cardiac cycles including at least ventriculardepolarizations, and one or more physical state signals indicative of aphysical state of the patient, and generating the patient conditionparameters comprises generating one or more baroreflex sensitivity (BRS)parameters being one or more measures of the BRS.
 20. The method ofclaim 19, wherein generating the patient condition parameters comprisesone or more of: detecting levels of physical activity or exertion of thepatient from the one or more physical state signals and generating theone or more BRS parameters each for a plurality of levels of thephysical activity or exertion; detecting a type of posture change of thepatient from the one or more physical state signals and stratifying theone or more BRS parameters by the detected type of posture change;detecting a magnitude or a duration of posture change of the patientfrom the one or more physical state signals and stratifying the one ormore BRS parameters by the detected magnitude of posture change; ordetecting a duration of posture change of the patient from the one ormore physical state signals and stratifying the one or more BRSparameters by the detected duration of posture change.